The use of wireless communication devices has continued to grow at a steady rate. As the use of wireless communication devices has grown, so has the demand for higher data rates. In order to keep up with the demand for higher data rates and growing demand for wireless communication service in general, service providers have been evolving their existing cellular networks to accommodate higher data rate services and applications.
Several approaches have been implemented to increase the data rates. One approach includes increasing the density of existing macro base stations, i.e., adding more macro base stations. Another approach involves increasing the cooperation between macro base stations. However, building a denser macro base station grid, while also enhancing cooperation between macro base stations is not a cost effective option as new macro base stations have to be installed in which the cost of installation and backhaul network requirements can be substantial.
Also, installing new macro base stations is not time efficient as new installations often take months to deploy. In urban areas, the cost and delay of installing new macro base stations may substantially outweigh the benefits of the installation. For example, a new macro base station may increase data throughput but the user will not get to experience this increased data throughput for the several months it takes to install the macro base station. Moreover, the installation may be specifically designed to service a wireless hotspot in which the wireless hotspot may move or shift by the time the installation is complete, thereby attenuating the benefits of the new installation. The effect of a denser macro station grid also leads to a significantly higher amount of signaling due to frequency of handovers for mobile devices moving at high speeds.
Another approach, involves deployment of smaller base stations within the macro base station grid. This approach is often referred to as a heterogeneous or homogeneous deployment in which the smaller base stations form the “micro” or “pico” layer. In particular, the smaller base stations are lower power nodes that absorb as many users as possible from the macro layers such as to offload the macro layer. The off loading of users from the macro layer allows for higher data rates in both the macro and pico layers. Also, this approach can serve users moving at high speed and can provide higher data rates to users in areas having a higher density of users such as a hotspot.
However, the deployment of lower power base stations or picocells is not without limits. For example, as loading of the network increases, the capacity of the network becomes interference limited, particularly for mobile device users near the cell edge between different cells. Optimizing signal quality for mobile device users, particularly at cell edge regions within a given cell while not overly penalizing neighboring cells is a major obstacle for both heterogeneous and homogenous deployments.
One solution for optimizing signal quality is for each cell to employ a greedy algorithm in which each cell tries to schedule as many of its users as possible in order to maximize its own aggregate throughput. However, if each cell employs this strategy, particularly for a heavily loaded network, this approach quickly leads to an interference limited scenario in which all cells and users suffer degraded performance, i.e., each cell maximizes its own aggregate throughput without considering the impact on neighbor cells.
In order to address the issues of employing a greedy algorithm methodology, a number of intercell interference coordination approaches have been defined including coordinated scheduling, coordinated beamforming, coordinated multipoint (CoMP) and joint processing between nodes. Typically these approaches are coupled with interference rejection approaches such as interference rejection combining (IRC) or serial interference cancellation (SIC), thereby allowing gains in throughput to be achieved.
However, intercell interference coordination coupled with interference rejection usually employs a metric that seeks to maximize some form of signal to interference plus noise ratio (SINR) of all users without considering the spatial and frequency orthogonality of the potential interference signals and the users that are being interfered with. In particular, optimization of SINR such as in IRC and SIC algorithms is based on the received interference at the cell of the user whose signal is being optimized and does not account for the impacts the optimization will have in causing interference in neighboring cells.